In this paper, we propose to learn the continuous solutions for spatio-temporal PDEs. Previous methods focused on either generating fixed resolution predictions or generating arbitrary resolution solutions on a fixed grid [1]}, [2]}. PDE models based on Multi-Layer Perceptrons (MLPs) can generate solutions at any point of the domain [3]}, [4]}, [5]}. However, without imposing a physics-motivated loss that constrains the predictions to follow the smoothness bias resulting from the PDE, MLPs become less competitive than CNN-based approaches especially when the PDE solutions have high-frequency information [6]}.
